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Developing a model for energy retrofit in large building portfolios: energy assessment, optimization and uncertainty

Author

Listed:
  • Laura Gabrielli
  • Aurora Ruggeri

Abstract

During the last years, a growing interest has pivoted around strategies and methodologies for energy efficiency in buildings. Nevertheless, the focus has always been on single properties, while the scientific research still lacks in solutions for building portfolios. Assets owners instead, would require reliable decisional tools to select the most effective retrofit solutions. This study intends to elaborate a model capable of identifying the optimal allocation of financial resources for energy enhancements in large building portfolios. The core idea is to assist and strongly orientate the decision-making process through a comprehensive new methodology. Some novelties characterize this research. First, the approach developed covers each aspect of energy retrofits, from preliminary analysis to construction and management. Second, the level of detail requested is not excessively burdensome, ensuring good reliability. Third, the approach is interdisciplinary, connecting statistical techniques (regression analysis), economic feasibility (life cycle costing, discounted cash flow analysis), optimization modelling (multi-attribute linear programming), and risk simulations (Monte Carlo simulation). The method developed has been implemented into a portfolio of 25 buildings in North Italy for testing and validation. It was possible to compare several design alternatives and reach for the best outcome. This demonstrated how the model could be successfully used in real applications. The most significant achievement in this study lies in its extreme flexibility, allowing confronting countless design scenarios until the optimal is attained. Another significant result is the synergic integration of traditional financial techniques with operational research. The last novelty is the employment of a two-dimensional Monte Carlo simulation to measure the risk, considering uncertainty as a structural part of the study. This methodology helps in verifying what are the best options from both an energy and economic point of view, giving priorities, time-distribution of interventions and optimizing the cash flows. The research could, therefore, be useful for portfolio managers, asset holders, private investors or public administrations, who have to plan and handle a series of energy efficiency actions.

Suggested Citation

  • Laura Gabrielli & Aurora Ruggeri, 2019. "Developing a model for energy retrofit in large building portfolios: energy assessment, optimization and uncertainty," ERES eres2019_203, European Real Estate Society (ERES).
  • Handle: RePEc:arz:wpaper:eres2019_203
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    File URL: https://eres.architexturez.net/doc/oai-eres-id-eres2019-203
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    Citations

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    Cited by:

    1. Domenico Enrico Massimo & Vincenzo Del Giudice & Alessandro Malerba & Carlo Bernardo & Mariangela Musolino & Pierfrancesco De Paola, 2021. "Valuation of Ecological Retrofitting Technology in Existing Buildings: A Real-World Case Study," Sustainability, MDPI, vol. 13(13), pages 1-35, June.
    2. Petkov, Ivalin & Mavromatidis, Georgios & Knoeri, Christof & Allan, James & Hoffmann, Volker H., 2022. "MANGOret: An optimization framework for the long-term investment planning of building multi-energy system and envelope retrofits," Applied Energy, Elsevier, vol. 314(C).
    3. Röck, Martin & Baldereschi, Elena & Verellen, Evelien & Passer, Alexander & Sala, Serenella & Allacker, Karen, 2021. "Environmental modelling of building stocks – An integrated review of life cycle-based assessment models to support EU policy making," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    4. Dell’Anna, Federico, 2021. "Green jobs and energy efficiency as strategies for economic growth and the reduction of environmental impacts," Energy Policy, Elsevier, vol. 149(C).
    5. Streicher, Kai Nino & Berger, Matthias & Panos, Evangelos & Narula, Kapil & Soini, Martin Christoph & Patel, Martin K., 2021. "Optimal building retrofit pathways considering stock dynamics and climate change impacts," Energy Policy, Elsevier, vol. 152(C).
    6. John Dadzie & Susan Djokoto, 2023. "Investigating Drivers Stimulating Demand for Green Renovation of Existing Buildings and Systems," Journal of Sustainable Development, Canadian Center of Science and Education, vol. 15(6), pages 1-88, May.
    7. Ma, Dingyuan & Li, Xiaodong & Lin, Borong & Zhu, Yimin, 2023. "An intelligent retrofit decision-making model for building program planning considering tacit knowledge and multiple objectives," Energy, Elsevier, vol. 263(PB).

    More about this item

    Keywords

    building portfolios; Energy Efficiency; life cycle costing; Linear Regression; Monte Carlo Simulation;
    All these keywords.

    JEL classification:

    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location

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